Evolution of MNE strategies amid China’s changing institutions: a thematic review
Bibliographic record
Abstract
Abstract As China’s economy rose to become the second largest in the world, its institutions did not converge with those of other advanced economies as predicted by many Western observers; instead, China developed a distinct form of state-led capitalism. As a result, how multinational enterprises (MNEs) engage with China’s changing institutional context needs to be revisited. To this end, we review 331 papers on MNE strategies and operations in China published in top international business and management journals between 2001 and 2022. We first introduce the path of institutional change and the opportunities and challenges it created for MNEs in China. We focus on six aspects of MNE strategies and operations: market entry, strategic alliances, innovation and knowledge sharing, global value chain strategies, guanxi and relationship management, and non-market strategies. Our analysis of China’s institutional trajectory and of MNE strategies and operations points to three persistent institutional mechanisms of concern for MNEs: challenges to organizational legitimacy, protection of property rights, and the enabling and directing aspect of institutions created by industrial policies. Insights from this analysis point to future research needs on institutional nonlinearities and discontinuities, linkages between inward and outward investments, and geopolitical influences on national institutions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".